[streamfunk191]

Training an LLM on timeseries feels limited, unless I’m missing something fundamental. If LLMs are basically prediction machines, if I have an LLM trained on cross-industry timeseries data, and I want to predict orange futures how much more effective can it be? (Genuine question). Secondly, Isn’t context hyper important? Such as weather, political climate etc.

[not2b]

A long time ago when I was a grad student, I got a consulting job with a radiologist who thought that he could use digital processing techniques to predict the options market well enough to make money. He didn't want to shell out for a real quant; he asked my prof it he knew anyone and I decided I could use the extra money. I came up with some techniques that appeared to produce a small profit, unfortunately it was a hair less than what he'd have to pay on commissions. He wanted to keep pushing but I decided to hang it up. I'm sure that there are people here who know far more about this than my decades-old experiments taught me.

So in principle it could work, but the problem is that these days, the big players are all doing high frequency trading with algorithms that try to predict market swings. And the big guys have an advantage: they are closer to the stock exchanges. They trade so fast that speed-of-light limitations affect who gets the trades in first. So I think the only people who could win with an LLM technique is someone who doesn't need to pay commissions (a market maker, Goldman Sachs or similar) with access to real time data, very close to the exchange so they get it fast.

[ezekiel68]

> Isn't context hyper important? Such as weather, political climate etc

(tongue firmly in cheek) Here is a bastardization of a memory of some video interview with a quantitative analyst, from over a decade ago:

"Show us your yield data and we'll TELL you your weather and political climate."